Noise
Noise in digital images is generally undesired. Many noise reduction algorithms exist that are appropriate for reducing the magnitude of noise in a digital image and improving the appearance of the image. An example of a noise reduction algorithm is the Sigma Filter, described by Jong-Sen Lee in the journal article “Digital Image Smoothing and the Sigma Filter”, Computer Vision, Graphics, and Image Processing, Vol. 24, 1983, pp. 255-269. Lee discloses a noise reduction filter that uses a non-linear pixel averaging technique sampled from a rectangular window about the center pixel. Pixels in the local neighborhood are either included or excluded from the numerical average on the basis of the difference between the pixel and the center pixel. The Sigma Filter was designed for image processing applications for which the dominant noise source is Gaussian additive noise. Signal dependent noise sources can be incorporated by making noise reduction control parameters a function of the signal strength. However, it can be difficult to determine the noise characteristics of a camera. One reason for this difficulty is the signal dependent noise from an image sensor varies greatly with the temperature of the image sensor.
Snow/Ice Detection
One fundamental task in image processing is to automatically determine the likelihood that regions (a connected group of pixel positions) or pixels of an image represent a specific material. This can be quite difficult, especially because the computer lacks the contextual information about the image. Current techniques use features derived from the image to generate belief maps (e.g. above cited, commonly-assigned U.S. patent application Ser. No. 10/747,597 in the case of sky detection). For example, a white region in an image may or may not represent snow. Such techniques often produce incorrect results because is it extremely difficult to produce features that can robustly be used for classification.
In a related task, sometimes it is necessary to classify an entire image. For example, in many cases it is useful to classify a digital image as either an outdoor image or an indoor image. Performing this task by using the image data alone is quite difficult and prone to errors.
Event Clustering
Individuals are amassing large collections of digital images. One useful way to organize these images is as a set of events, where each event includes a set of digital images that are related by the event at which they were captured. Manual event clustering can be labor-intensive. Automatic methods of event segmentation focus almost exclusively on using the digital images' timestamps. For example, an event clustering algorithm is described by Cooper et al. in “Temporal Event Clustering for Digital Photo Collections,” MM '03, Nov. 2-8, 2003, Berkeley, Calif. This method leaves much room for improvement. It does not take advantage of the fact that generally the environment at an event is relatively stable.
Search
In order to find a specific image from a collection of digital images, it is necessary to search for it. Manual searching is time-consuming and tiring. Automatic searching relies on a combination of matching between a query and features extracted from the image or labels that are associated with the image. For example, U.S. Pat. No. 6,480,840 describes a method for image retrieval based on image content similarity. Such a method can require several iterations and frequently produces incorrect results. No information about ambient air characteristics is used to improve the results.